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Evaluating drug-adverse event causality based on an integration of heterogeneous drug safety causality models

机译:基于非均相药物安全因果区模型的整合评估药物 - 不良事件因果关系

摘要

Mechanisms are provided that implement a plurality of heterogeneous causality models and a metaclassifier for predicting a likelihood of causality between a drug and an adverse event (AE). The plurality of heterogenous causality models process drug information to generate a plurality of risk predictions for a drug and AE pair. The risk predictions include a risk score or a risk label indicating a probability of the AE occurring with use of the drug. The plurality of heterogenous causality models provide risk predictions, associated with the drug and AE pair, to a metaclassifier which generates a single causality score value indicative of a probability of causality between the drug and the AE, of the drug and AE pair, based on an aggregation of the risk predictions from the plurality of heterogenous causality models. The metaclassifier outputs the single causality score value in association with information identifying the drug and AE pair.
机译:提供了用于实现多个异质因果态模型和元丙匹箱的机制,用于预测药物和不良事件(AE)之间的因果关系的可能性。 多个异质因果关系模型处理药物信息以产生药物和AE对的多个风险预测。 风险预测包括风险评分或风险标签,其表明使用药物发生的AE的可能性。 多个异质因果态模型提供与药物和AE对相关的风险预测,所述危险预测与药物和AE对产生的单一因果区分数值,其基于 来自多个异构因果模型的风险预测的聚集。 MetaClassifier与识别药物和AE对的信息相关联地输出单个因果区分值。

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